Method for selectively combining multiple membranes for assembly into test strips

ABSTRACT

A method for selectively combining multiple membranes for assembly into test strips (such as visual blood glucose test strips with side-by-side membranes). The method includes first measuring a plurality of color parameters (e.g., L*, a* and b* color parameters) associated with membrane samples from at least two membrane lots. Next, response characteristics (e.g., blood glucose response levels) are simulated for a speculative test strip that includes, for purposes of the simulation, combined multiple membranes tentatively selected from the at least two membrane lots. The simulated response characteristics are based on the measured plurality of color parameters of the tentative selection of combined multiple membranes. Optionally, the simulated response characteristics can also be based on simulated color parameters of the tentative selection of combined multiple membranes. Subsequently, assembly of the at least two membrane lots into a test strip with combined membranes is contingent on acceptable simulated response characteristics. Any suitable color parameters can be employed. The method can be used to selectively combine two or more membranes based on any number of color parameters. The assembled test strips can be used to measure glucose, cholesterol, proteins, ketones, phenylalanine or enzymes in blood, urine, saliva or other biological fluid, as well as sample fluid characteristics (e.g., pH and alkalinity).

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] This invention relates, in general, to methods for themanufacturing of test strips and, in particular, to methods forselectively combining multiple membranes for assembly into test strips.

[0003] 2. Description of the Related Art

[0004] Various test strips have been developed for measuring theconcentration of certain analytes in fluids and/or chemical propertiesof a fluid (e.g., pH or alkalinity). Such test strips can be used tomeasure, for example, glucose, cholesterol, proteins, ketones,phenylalanine or enzymes in blood, urine or saliva. These test stripsfrequently include multiple membranes that facilitate the determinationof the analyte concentration or chemical property. For example, U.S.Pat. No. 6,162,397, which is fully incorporated herein by reference,describes a visual blood glucose test strip with two side-by-sidemembranes (i.e., paired membranes). Such paired membranes containreagents which react with blood glucose to form visibly different colors(see also, Sherwood, M. et al., A New Reagent Strip (Visidex™) forDetermination of Glucose in Whole Blood, Clinical Chemistry, 438-446[1983]). A user can subsequently compare the two colors thus formed to acalibrated color chart (e.g., a color chart that includes sets of pairedcolor pads) to ascertain blood glucose concentration.

[0005]FIG. 1 is a top plan view of a conventional visual blood glucosetest strip 10. FIG. 2 depicts an exemplary calibrated color chart 200for use with visual blood glucose test strip 10. Visual blood glucosetest strip 10 includes a spreading top layer 12, an intermediate layer14 with two membranes 14 a and 14 b (i.e., paired membranes 14 a and 14b), and a support layer 16 with openings 16 a and 16 b. In operation, auser applies a blood sample to spreading top layer 12. As the bloodsample penetrates spreading top layer 12, the blood sample spreads outand is substantially and uniformly distributed to paired membranes 14 aand 14 b. Glucose in the blood sample reacts with reagents in the pairedmembranes 14 a, 14 b, as it passes toward support layer 16, to formvisually different colors in each of the paired membranes. The colorsare viewed through openings 16 a and 16 b and compared with the pairedcolor pads 202 a-202 h of calibrated color chart 200 to determine theblood glucose concentration of the blood sample. For the purpose ofexplanation only, calibrated color chart 200 in FIG. 2 is depicted toinclude eight sets of paired color pads (202 a through 202 h), eachcorresponding to one of eight targeted blood glucose test levels (e.g.,25, 50, 80, 120, 180, 240, 400 and 600 mg/dL). A user obtains a resultby visually matching the paired membranes of a reacted visual bloodglucose test strip to a paired set of color pads on calibrated colorchart 200.

[0006] For quality assurance purposes during manufacturing, each lot oftest strips with multiple membranes will customarily undergo acceptancetesting in order to verify the accuracy of results obtained therewith.Such acceptance testing typically relies on any of a variety of standardcolor definition systems that specify color parameters for individualcolors (for example, one of the color systems defined by the CommissionInternationale de l'Eclairage (CIE) including the systems based on theL*a*b* color space and L*C*h color space). Methods for such acceptancetesting are described in co-pending U.S. patent application Ser. No.______ (tentatively identified by Attorney's Docket No. LFS-243 andincorporated herein by reference as if fully set forth) entitled“Acceptance Testing Method for Sets of Multiple Colored Workpieces.”

[0007] The acceptance testing of a lot of test strips is conventionallyconducted after multiple membranes (each from a separate lot ofmembranes) have been combined and assembled into the lot of test strips.However, a particular combination of multiple membranes that has beenassembled into a lot of test strips may not be optimal or evenacceptable in terms of result accuracy. If a lot of test stripsundergoing acceptance testing does not meet acceptance criteria forresult accuracy, the entire lot of test strips is subject to rejection.

[0008] Still needed in the field, therefore, is a method for selectivelycombining multiple membranes for assembly into a test strip thatminimizes test strip lot rejection. In addition, the method should beobjective and yet account for user-related visual effects.

SUMMARY OF INVENTION

[0009] The present invention provides a method for selectively combiningmultiple membranes for assembly into a test strip that minimizes teststrip lot rejection during acceptance testing. The method isinstrument-based and, therefore, objective, yet capable of accountingfor user-related visual effects.

[0010] An method for selectively combining multiple membranes forassembly into a test strip according to one exemplary embodiment of thepresent invention includes first measuring a plurality of colorparameters associated with membrane samples from at least two lots (a“first lot” and a “second lot”) of membranes. Although the method isdetailed below in terms of CIE L*a*b* color parameters and paired (i.e.,two side-by-side) membranes of a visual blood glucose test strip forease of description, once apprised of the present disclosure one skilledin the art will recognize that color parameters of other color systemscan be employed and/or a different quantity of multiple membranesselectively combined for assembly into a test strip. For example,methods according to the present invention can be employed toselectively combine “m” multiple membranes for assembly into a teststrip, where “m” is two or greater, based on “n” color parameters, where“n” can be any number.

[0011] It is also contemplated that methods in accordance with thepresent invention can be easily employed during the manufacturing oftest strips with multiple membranes that are used to measure, forexample, (i) glucose, cholesterol, proteins, ketones, phenylalanine orenzymes in blood, urine, saliva or other biological fluid and/or (ii)sample fluid characteristics such as pH and alkalinity.

[0012] Next, a response characteristic(s) of a speculative (i.e.,hypothetical) test strip with multiple membranes is simulated. Forpurposes of the simulation, the speculative test strip includes acombination of multiple membranes that have been tentatively selectedfrom the at least first lot of membranes and second lot of membranes.Furthermore, the simulated response characteristic(s) is based on themeasured plurality of color parameters of the combined multiplemembranes that have been tentatively selected. It should be noted thatat this step of the method, the speculative test strip has not beenphysically assembled but is an imaginary construct for which theresponse characteristic(s), such as analyte concentration(s), aresimulated.

[0013] The response characteristic of the speculative test strip that issimulated in methods according to the present invention can be anyresponse characteristic known to one skilled in the art. Such asimulated response characteristic includes, but is not limited to, ananalyte concentration of a biological fluid sample (e.g., a bloodglucose concentration), a chemical property of a fluid sample (such aspH or alkalinity) and a statistical property (e.g., a measure of thevariance or accuracy of the speculative test strip).

[0014] Next, the at least first and second lot of membranes, from whichthe combined multiple membranes were tentatively selected, is assembledinto a test strip with combined multiple membranes. However, thisassembly is contingent on an acceptable simulated responsecharacteristic(s) for the speculative test strip that included thetentatively selected combined multiple membranes. If the simulatedresponse characteristics are not acceptable, assembly of the multiplemembranes into a test strip does not proceed and an alternativetentative selection of combined multiple membranes from anotherassortment of membrane lots can be made.

[0015] Since, in methods according to the present invention, multiplemembranes are selectively combined for assembly into a test strip basedon response characteristic(s) that are simulated prior to assembly,methods according to the present invention can optimize the performancecharacteristics (e.g., accuracy) of the assembled test strips and reducetest strip lot rejection during any subsequent acceptance testing. Inaddition, since the simulated response characteristic(s) are based oninstrument-based measurements of color parameters, the methods areobjective. Furthermore, algorithms that account for user-related visualeffects can be employed during the simulation of the simulated responsecharacteristic(s).

BRIEF DESCRIPTION OF DRAWINGS

[0016] A better understanding of the features and advantages of thepresent invention will be obtained by reference to the followingdetailed description that sets forth illustrative embodiments, in whichthe principles of the invention are utilized, and the accompanyingdrawings of which:

[0017]FIG. 1 is a bottom plan view of a conventional visual bloodglucose test strip;

[0018]FIG. 2 is a simplified top plan view of an exemplary calibratedcolor chart as may be used in conjunction with the conventional visualblood glucose test strip of FIG. 1;

[0019]FIG. 3 is a flow diagram illustrating a sequence of steps in aprocess according to one exemplary embodiment of the present invention;and

[0020]FIG. 4 is a flow diagram illustrating a sequence of step in aprocess according to another exemplary embodiment of the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

[0021]FIG. 3 is a flow diagram illustrating a sequence of steps in aprocess 300 for selectively combining multiple membranes for assemblyinto a test strip according to an exemplary embodiment of the presentinvention. Process 300 includes measuring a plurality of colorparameters associated with membrane samples from at least two lots(i.e., a first lot and a second lot) of membranes, as set forth in step310. The measuring of the color parameters can be accomplished usinginstruments and methods well known to one skilled in the art. Forexample, commercially available color parameter instruments such as aMinolta Chromameter model CR-241 (available from Minolta Co. Ltd, Osaka,Japan) or commercially available spectrophotometers can be employed tomeasure the color parameters.

[0022] The color parameters associated with the membrane samplesinclude, but are not limited to, L*a*b* color parameters of the L*a*b*color space; X, Y and Z color parameters of the XYZ tristimulus space;Y, x and y color parameters of the Yxy color space; L*C*h* colorparameters of the L*C*h* color space, and HL, a and b color parametersof the Hunter Lab color system. In the circumstance that L*a*b* colorparameters are measured, the result of such a measurement will be threediscrete color parameters for each of the membrane samples. For example,if there are two membrane samples (i.e., one membrane sampled from afirst membrane lot and another membrane sampled from a second membranelot) the result will be six discrete color parameters. Whereas, if thereare three membrane samples, with each having been selected from one ofthree membrane lots, the result will be nine discrete color parameters.In general, for “q” membrane samples on which “n” color parameters aremeasured, the result will be q·n discrete color parameters.

[0023] Once apprised of the present disclosure, one skilled in the artwill recognize that the membrane samples from the at least first andsecond membrane lots can take a variety of forms. For example, themembranes samples can include a plurality of membranes sampled from afirst lot and a plurality of membranes sampled from a second lot, witheach of the plurality of membranes from each lot having been reactedwith a fluid sample containing a different concentration of analyte.Furthermore, methods in accordance with the present invention can beeasily employed during the manufacturing of test strips with multiplemembranes that are used to measure, for example, (i) the level ofglucose, cholesterol, proteins, ketones, phenylalanine or enzymes inblood, urine, saliva or other biological fluid and/or (ii) sample fluidcharacteristics such as pH and alkalinity.

[0024] Next, as set forth in step 320, response characteristics of aspeculative test strip are simulated. The speculative test stripincludes, for purposes of simulating the response characteristics,combined multiple membranes tentatively selected from the at least firstand second lots of membranes. In addition, the simulated responsecharacteristics are based on the measured plurality of color parametersfrom step 310.

[0025] It is contemplated that the combined multiple membranes that aretentatively selected at step 320 can originate from a sub-set of thelots from which membrane samples were measured at step 310. For example,if membrane samples from 10 lots were measured, then the “combinedmultiple membranes” for a speculative test strip with two membranes willbe tentatively selected from only a two lot sub-set of the 10 lots.Similarly, for a speculative test strip with m membranes, the “combinedmultiple membranes” will be tentatively selected from an m lot sub-setof the lots from which membrane samples were measured.

[0026] The response characteristics of the speculative test strip can besimulated, for example, utilizing an algorithm that relates a responsecharacteristic (P) to the color parameters (CP₁ to CP_(m n)). Ingeneral, such an algorithm takes the form of equation (1) below:

P=f(CP ₁ , CP ₂ , . . . CP _(m·n))  (1)

[0027] where: P is the response characteristic; and

[0028] CP₁, CP₂ . . . CP_(m·n) are the m·n color parameters associatedwith the m selected multiple membranes.

[0029] Equation (1) essentially converts the color parameters of theselected multiple membranes into a simulated response characteristic(e.g., a simulated analyte concentration) that would be produced by thespeculative test strip that includes those multiple membranes.

[0030] In order to more accurately simulate response characteristics ofa speculative test strip, by taking into account fixed, random andresidual sources of variability associated with sampling and the testmethod employed for measurement of color parameters, it can bebeneficial to optionally simulate response characteristics based notonly on the measured color parameters but also based on color parametersthat are themselves simulated (i.e., simulated color parameters). Thesimulated color parameters can be simulated (obtained) using, forexample, an experimentally-derived multivariate mixed model equation ofthe general form:

R=Yβ+Zγ+e  (2)

[0031] where:

[0032] R is an N×1 vector of color parameters

[0033] β is a b×1 vector of fixed effect coefficients

[0034] Y is an N×b matrix of independent covariates

[0035] γ is a g×1 vector of random effect coefficients

[0036] Z is an N×g matrix of random effects; and

[0037] e is an N×1 vector of residual errors.

[0038] In order to effectively increase the distribution of simulatedresponse characteristics for the speculative test strip, it can bebeneficial to optionally simulate response values that are intermediateto those of measured color parameters: This can be accomplished, forexample, by fitting a linear regression model to the simulated responsecharacteristic(s) with a reference response characteristic(s) as anindependent variable. Such a linear regression model has the generalform:

Simulated response characteristic=A*(reference responsecharacteristic)+B+e  (3)

[0039] where:

[0040] A is the slope of the linear regression

[0041] B is the intercept of the linear regression; and

[0042] e is an error term.

[0043] Furthermore, to account for user-related visual effects, it canbe optionally beneficial to estimate the variance of the simulatedresponse characteristics and to adjust the variance to account for theuser-related visual effects. The variances of the simulated responsecharacteristics can be subsequently computed at each level of thereference response characteristic. To adjust for a larger variation dueto any user-related visual effects, the standard deviation of thesimulated response characteristic can then be multiplied by an inflationfactor. This can be accomplished using the standard normal deviate, asfollows:

Predicted analyte level=A*(reference analyte value)+B+ε

[0044] Where, for reference response values associated with measuredcolor parameters:

ε=N(0,1)*inflation factor*Sqrt(test level variance)

[0045] and where, for reference response values associated withsimulated color parameters that are intermediate to those of measuredcolor parameters:

ε=N(0,1)*inflation factor*Sqrt(0.5*(V1+V3))

[0046] where:

[0047] V1 is the variance for the lower values, associated with measuredcolor parameters, bracketing the intermediate level

[0048] V3 is the variance for the higher values, associated withmeasured color parameters, bracketing the intermediate level, and

[0049] N(0,1) is a standard normal deviate.

[0050] After the response characteristics have been simulated, the atleast first and second lot of membranes, from which the tentativelycombined multiple membranes were sampled, are assembled into a teststrip with combined membranes. The assembly is, however, contingent onthe simulated response characteristics being acceptable, as set forth instep 330. The acceptability of the simulated response characteristicscan be determined based on comparison to a specification(s) and/or anyknown method of assessing the accuracy of the simulated responsecharacteristics such as a Clarke's Error Grid analysis.

[0051] A Clark's Error Grid analysis provides a method to access theclinical accuracy of a blood glucose monitoring device (e.g., a visualblood glucose test strip). The error grid of such an analysiscategorizes a device's response against a reference value into one offive clinical accuracy zones (i.e., zones A-E). Where zone A indicatesclinically accurate results; zone B indicates results that are notclinically accurate but pose minimal risk to patient health; and zones Cthrough E indicate clinically inaccurate results that pose increasingpotential risk to patient health (see Clarke, William L. et al.,Evaluating Clinical Accuracy of Systems for Self-Monitoring of BloodGlucose, Diabetes Care, Vol. 10 No. 5, 622-628 [1987]). Specificationscan be developed based on the proportion of results falling within thevarious error grid zones or more rigorously, the lower confidence boundof the proportion (e.g., the lower 90% Confidence Limit for theproportion of data points in Zone A+Zone B is at least 0.90 or greater).

[0052] Those skilled in the art will appreciate that methods accordingthe present invention can be beneficially employed to select acombination of multiple membranes for assembly into a test strip bymeasuring the color parameters of membranes sampled from a plurality ofmembrane lots and then choosing only those combinations of multiplemembranes that provide for a test strip of acceptable accuracy forassembly into a test strip. Therefore, this method optimizes theaccuracy of the assembled test strips.

[0053] Exemplary Method for the Selective Combining of Two Membranes forAssembly into a Visual Blood Glucose Test Strip

[0054] Referring to FIGS. 1 and 4, a process 400 that was developed forselectively combining two membranes into a visual blood glucose teststrip 10 is described. The side-by-side membranes of such a visual bloodglucose test strip can be manufactured, for example, using well knownweb-based process in which tracks are a source of variation.

[0055] Three color parameters (i.e., L*, a* and b* color parameters)were measured on multiple (i.e., twelve) membranes, with six of thetwelve membranes having been sampled from a first lot of membranes(i.e., a “blue membrane” lot) and the other six of the twelve havingbeen sampled from a second lot of membranes (i.e., a “yellow” membranelot), using a Minolta Chromameter. See step 410. Prior to measurement,the membrane samples from each of the blue and yellow membrane lots werereacted with blood glucose at a level of either 50, 80, 120, 180, 270 or400 mg/dL.

[0056] Using the following linear mixed model derived from Equation (2)above, simulated L*, a* and b* color parameters were obtained for testedblood glucose levels of 50, 80, 120, 180, 270 or 400 mg/dL (see step420).

r _(ijkl)=α_(i)+β_(i) * Y _(ijkl) +b _(j) +t _(k) +e _(ijkl)

[0057] where:

[0058] i=1, 2, . . . , 6 (the number of glucose levels used to derivethe linear mixed model);

[0059] j=1, 2, . . . , 4 (the number of different types of blood used toderive the linear mixed model);

[0060] k=1, 2, . . . , 4 (the number of tracks used in manufacturingtest strips);

[0061] l=1, 2, . . . , 4 (the number of replicates tested to derive thelinear mixed model);

[0062] b_(j)˜N(0,σ_(B) ²), t_(k)˜N(0, σ_(T) ²) and e_(ijkt)˜N(0, σ²);

[0063] r_(ijkl) is any one of the L*, a* and B* color parameters,

[0064] α is an intercept of the linear mixed model,

[0065] β is a slope of the linear mixed model

[0066] Y_(ijkl) is an average reference instrument analyte value at aparticular glucose level; and

[0067] σ_(B) ², σ_(T) ² and σ² are the variances for blood, track andresidual, respectively.

[0068] The coefficients of the above equation are unique to amanufactured membrane at each tested reference analyte level and vary bymembrane lot and manufacturing run. The coefficient values can be, forexample, experimentally derived from measured color parameter data.

[0069] Next, as set forth in step 430, simulated responsecharacteristics (i.e., simulated blood glucose level responses) werecalculated from the simulated color parameters using the followingequation that was derived from Equation (1): $\begin{matrix}{R = \left( {28.874823 - {0.245112*{{blue}L}} - {0.178014*{yellL}} -} \right.} \\{{{0.392156*{yellb}} + {0.011033*{{blue}a}^{2}} + {0.003151*{yellb}^{2}} +}} \\{{{0.003091*{yellL}*{{blue}L}} - {0.002856*{yella}*{yellb}} -}} \\\left. {0.004318*{yellb}*{{blue}b}} \right)^{2}\end{matrix}$

[0070] While this example illustrates the use of simulated colorparameters for simulating response characteristics, one skilled in theart will recognize that measured color parameters and/or simulated colorparameters can be utilized to simulate response characteristics of aspeculative test strip.

[0071] Since an objective of process 400 is the selection of multiplemembranes for assembly into a visual blood glucose test strip, it wasdesirable to estimate the variability of the simulated responsecharacteristics and to adjust that variability to account foruser-related visual effects (e.g., the additional variability associatedwith a user's visual comparison of a visual blood glucose test strip toa calibrated color chart). This was accomplished through the use of alinear regression model (derived from equation (3)) that used thesimulated response characteristic (i.e., simulated glucose level) as aresponse and reference measurements as the independent variable. Theresulting equation was:

Simulated glucose value=intercept+slope*(average referencemeasurement)+e

[0072] where “e” is an error term calculated using the standard normaldeviate as follows Glucose Point e 50 N(0,1)* 1.5*Sqrt(V50) 65 N(0,1)*1.5*Sqrt(0.5*(V50 + V80)) 80 N(0,1)* 1.5*Sqrt(V80) 100 N(0,1)*1.5*Sqrt(0.5*(V80 + V120)) 120 N(0,1)* 1.5*Sqrt(V120) 150 N(0,1)*1.5*Sqrt(0.5*(V120 + V180)) 180 N(0,1)* 1.5*Sqrt(V180) 225 N(0,1)*1.5*Sqrt(0.5*(V180 + V270)) 270 N(0,1)* 1.5*Sqrt(V270) 335 N(0,1)*1.5*Sqrt(0.5*(V270 + V400)) 400 N(0,1)* 1.5*Sqrt(V400)

[0073] and where N(0,1) is a standard normal deviate.

[0074] The coefficients of the equations are unique to a specificpairing of manufactured membrane lots and vary by membrane lot andmanufacturing run. The coefficient values are experimentally derivedfrom measured color parameter data.

[0075] Next, as set forth in step 440, a Clarke's Error Grid analysiswas performed on the simulated response characteristics to determine theacceptability thereof. Contingent on the acceptability of the simulatedresponse characteristics, a test strip with multiple membranes wasassembled.

[0076] It should be understood that the various alternatives to theembodiments of the invention described herein may be employed inpracticing the invention. It is intended that the following claimsdefine the scope of the invention and that methods within the scope ofthese claims and their equivalents be covered thereby.

What is claimed is:
 1. A method for selectively combining multiplemembranes for assembly into test strips comprising: measuring aplurality of color parameters associated with membrane samples from atleast a first lot of membranes and a second lot of membranes; simulatingresponse characteristics of a speculative test strip that includes, forpurposes of simulating the response characteristics, combined multiplemembranes tentatively selected from the at least first lot of membranesand second lot of membranes, the simulated response characteristicsbeing based on the measured plurality of color parameters of thetentative selection of combined multiple membranes; and assembling theat least first and second lot of membranes from which the combinedmultiple membranes were tentatively selected into a test strip withcombined membranes, the assembly contingent on acceptable simulatedresponse characteristics.
 2. The method of claim 1, wherein thesimulating step utilizes at least a first algorithm in the form of:P=f(CP ₁ , CP ₂ , . . . CP _(m·n)) where: P is the simulated responsecharacteristic; and CP₁, CP₂ . . . CP_(m·n) are the m·n color parametersassociated with m multiple membranes.
 3. The method of claim 1, whereinthe simulating step further includes estimating the variances of thesimulated response characteristics and adjusting the variances foruser-related visual effects.
 4. The method of claim 1, wherein thesimulating step simulates response characteristics that are furtherbased on simulated color parameter data.
 5. The method of claim 4,wherein the simulating step simulates response characteristics that arefurther based on simulated color parameter data obtained utilizing anexperimentally-derived multivariate mixed model equation of the form:R=Yβ+Zγ+e where: R is an N×1 vector of color parameter responses; β is ab×1 vector of fixed effect coefficients; Y is an N×b matrix ofindependent covariates; γ is a g×1 vector of random effect coefficients;Z is an N×g matrix of random effects; and e is a N×1 vector of residualerrors.
 6. The method of claim 1, wherein the assembling step includesassembly contingent on acceptable simulated response characteristics asdetermined by comparison of the simulated response characteristics to aspecification.
 7. The method of claim 1, wherein the assembling stepincludes assembly contingent on acceptable simulated responsecharacteristics as determined using a Clarke's Error Grid analysis. 8.The method of claim 1, wherein the measuring step measures L*, a* and b*color parameters of the L*a*b* color space.
 9. The method of claim 1,wherein the measuring step is accomplished using a chromameter.
 10. Themethod of claim 1, wherein the measuring step is accomplished using aspectrophotometer.
 11. The method of claim 1, wherein the measuring stepmeasures X, Y and Z color parameters of the XYZ tristimulus space. 12.The method of claim 1, wherein the measuring step measures Y, x and yvalues of the Yxy color space.
 13. The method of claim 1, wherein themeasuring step measures L, C and h values of the L*C*h color space. 14.The method of claim 1, wherein the measuring step measures HL, a and bvalues of the Hunter Lab color system.
 15. The method of claim 1,wherein the measuring step includes measuring a plurality of colorparameters on membrane samples that have been reacted with an analyte.16. A method for selectively combining multiple membranes for assemblyinto visual test strips, the visual test strips for use in determinationof analyte concentration in a biological fluid, the method comprising:measuring L*, a* and b* color parameters associated with membranesamples from at least a first lot of membranes and a second lot ofmembranes; obtaining simulated color parameters associated with themembrane samples selected from at least the first lot of membranes andthe second lot of membranes; simulating response characteristics of aspeculative test strip that includes, for purposes of simulating theresponse characteristics, combined multiple membranes tentativelyselected from the at least first lot of membranes and second lot ofmembranes, the simulated response characteristics being based on themeasured plurality of color parameters and the simulated colorparameters of the tentative selection of combined multiple membranes;and assembling the at least first and second lot of membranes into atest strip with combined membranes contingent on acceptable simulatedresponse characteristics.
 17. The method of claim 16, wherein thesimulating step utilizes at least a first algorithm in the form of:P=f(CP ₁ , CP ₂ , . . . CP _(m·n)) where: P is the simulated responsecharacteristic; and CP₁, CP₂ . . . CP_(m·n) are the m·n color parametersassociated with m multiple membranes.
 18. The method of claim 16,wherein the simulating step further includes estimating the variances ofthe simulated response characteristics and adjusting the variances foruser-related visual effects.
 19. The method of claim 16, wherein thesimulating step simulates response characteristics that are furtherbased on simulated color parameter data obtained utilizing anexperimentally-derived multivariate mixed model equation of the form:R=Yβ+Zγ+e where: R is an N×1 vector of color parameter responses; β is ab×1 vector of fixed effect coefficients; Y is an N×b matrix ofindependent covariates; γ is a g×1 vector of random effect coefficients;Z is an N×g matrix of random effects; and e is a N×1 vector of residualerrors.
 20. The method of claim 16, wherein the assembling step includesassembly contingent on acceptable simulated response characteristics asdetermined by comparison of the simulated response characteristics to aspecification.
 21. The method of claim 16, wherein the assembling stepincludes assembly contingent on acceptable simulated responsecharacteristics as determined using a Clarke's Error Grid analysis.